Overview

Dataset statistics

Number of variables20
Number of observations50
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.9 KiB
Average record size in memory203.1 B

Variable types

Numeric15
Categorical4
Boolean1

Alerts

Unnamed: 0 is highly overall correlated with ArtistHigh correlation
Energy is highly overall correlated with LoudnessHigh correlation
Loudness is highly overall correlated with EnergyHigh correlation
Speechiness is highly overall correlated with ChannelHigh correlation
Instrumentalness is highly overall correlated with ChannelHigh correlation
Views is highly overall correlated with LikesHigh correlation
Likes is highly overall correlated with ViewsHigh correlation
Artist is highly overall correlated with Unnamed: 0 and 1 other fieldsHigh correlation
Track is highly overall correlated with Title and 1 other fieldsHigh correlation
Title is highly overall correlated with Track and 1 other fieldsHigh correlation
Channel is highly overall correlated with Speechiness and 5 other fieldsHigh correlation
Licensed is highly overall correlated with ChannelHigh correlation
Licensed is highly imbalanced (59.8%)Imbalance
Artist is uniformly distributedUniform
Track is uniformly distributedUniform
Title is uniformly distributedUniform
Channel is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
Key has 3 (6.0%) zerosZeros
Instrumentalness has 23 (46.0%) zerosZeros

Reproduction

Analysis started2023-05-16 20:09:48.094629
Analysis finished2023-05-16 20:10:07.468469
Duration19.37 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11596.38
Minimum126
Maximum20308
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-05-17T01:40:07.559108image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum126
5-th percentile199.75
Q110777
median13036
Q315392
95-th percentile19244.6
Maximum20308
Range20182
Interquartile range (IQR)4615

Descriptive statistics

Standard deviation6037.5739
Coefficient of variation (CV)0.52064298
Kurtosis-0.19016506
Mean11596.38
Median Absolute Deviation (MAD)2355
Skewness-0.95970362
Sum579819
Variance36452299
MonotonicityNot monotonic
2023-05-17T01:40:07.699884image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1147 1
 
2.0%
12480 1
 
2.0%
14076 1
 
2.0%
17951 1
 
2.0%
157 1
 
2.0%
15529 1
 
2.0%
17195 1
 
2.0%
13329 1
 
2.0%
404 1
 
2.0%
20308 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
126 1
2.0%
154 1
2.0%
157 1
2.0%
252 1
2.0%
365 1
2.0%
366 1
2.0%
404 1
2.0%
744 1
2.0%
1147 1
2.0%
2466 1
2.0%
ValueCountFrequency (%)
20308 1
2.0%
20304 1
2.0%
20303 1
2.0%
17951 1
2.0%
17286 1
2.0%
17195 1
2.0%
16668 1
2.0%
16637 1
2.0%
16488 1
2.0%
15559 1
2.0%

Artist
Categorical

HIGH CORRELATION  UNIFORM 

Distinct39
Distinct (%)78.0%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
Ed Sheeran
 
3
CoComelon
 
3
Shakira
 
2
Charlie Puth
 
2
Daddy Yankee
 
2
Other values (34)
38 

Length

Max length17
Median length14
Mean length10.06
Min length2

Characters and Unicode

Total characters503
Distinct characters49
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)60.0%

Sample

1st rowLuis Fonsi
2nd rowDaddy Yankee
3rd rowEd Sheeran
4th rowCharlie Puth
5th rowWiz Khalifa

Common Values

ValueCountFrequency (%)
Ed Sheeran 3
 
6.0%
CoComelon 3
 
6.0%
Shakira 2
 
4.0%
Charlie Puth 2
 
4.0%
Daddy Yankee 2
 
4.0%
Katy Perry 2
 
4.0%
Justin Bieber 2
 
4.0%
DJ Snake 2
 
4.0%
Passenger 2
 
4.0%
Eminem 1
 
2.0%
Other values (29) 29
58.0%

Length

2023-05-17T01:40:07.827051image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ed 3
 
3.6%
cocomelon 3
 
3.6%
sheeran 3
 
3.6%
perry 2
 
2.4%
passenger 2
 
2.4%
dj 2
 
2.4%
bieber 2
 
2.4%
justin 2
 
2.4%
snake 2
 
2.4%
katy 2
 
2.4%
Other values (56) 61
72.6%

Most occurring characters

ValueCountFrequency (%)
a 55
 
10.9%
e 46
 
9.1%
n 40
 
8.0%
i 34
 
6.8%
34
 
6.8%
r 32
 
6.4%
l 24
 
4.8%
o 23
 
4.6%
h 16
 
3.2%
s 16
 
3.2%
Other values (39) 183
36.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 376
74.8%
Uppercase Letter 92
 
18.3%
Space Separator 34
 
6.8%
Decimal Number 1
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 55
14.6%
e 46
12.2%
n 40
10.6%
i 34
9.0%
r 32
8.5%
l 24
 
6.4%
o 23
 
6.1%
h 16
 
4.3%
s 16
 
4.3%
t 14
 
3.7%
Other values (15) 76
20.2%
Uppercase Letter
ValueCountFrequency (%)
S 12
13.0%
C 11
 
12.0%
P 9
 
9.8%
D 7
 
7.6%
E 6
 
6.5%
M 5
 
5.4%
J 4
 
4.3%
W 4
 
4.3%
F 3
 
3.3%
O 3
 
3.3%
Other values (12) 28
30.4%
Space Separator
ValueCountFrequency (%)
34
100.0%
Decimal Number
ValueCountFrequency (%)
5 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 468
93.0%
Common 35
 
7.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 55
 
11.8%
e 46
 
9.8%
n 40
 
8.5%
i 34
 
7.3%
r 32
 
6.8%
l 24
 
5.1%
o 23
 
4.9%
h 16
 
3.4%
s 16
 
3.4%
t 14
 
3.0%
Other values (37) 168
35.9%
Common
ValueCountFrequency (%)
34
97.1%
5 1
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 501
99.6%
None 2
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 55
 
11.0%
e 46
 
9.2%
n 40
 
8.0%
i 34
 
6.8%
34
 
6.8%
r 32
 
6.4%
l 24
 
4.8%
o 23
 
4.6%
h 16
 
3.2%
s 16
 
3.2%
Other values (37) 181
36.1%
None
ValueCountFrequency (%)
ó 1
50.0%
Ø 1
50.0%

Track
Categorical

HIGH CORRELATION  UNIFORM 

Distinct44
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
Despacito
 
2
This Is What You Came For
 
2
See You Again (feat. Charlie Puth)
 
2
Lean On
 
2
Taki Taki (with Selena Gomez, Ozuna & Cardi B)
 
2
Other values (39)
40 

Length

Max length99
Median length30
Mean length19.36
Min length4

Characters and Unicode

Total characters968
Distinct characters65
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)76.0%

Sample

1st rowDespacito
2nd rowDespacito
3rd rowShape of You
4th rowSee You Again (feat. Charlie Puth)
5th rowSee You Again (feat. Charlie Puth)

Common Values

ValueCountFrequency (%)
Despacito 2
 
4.0%
This Is What You Came For 2
 
4.0%
See You Again (feat. Charlie Puth) 2
 
4.0%
Lean On 2
 
4.0%
Taki Taki (with Selena Gomez, Ozuna & Cardi B) 2
 
4.0%
Calma - Remix 2
 
4.0%
New Rules 1
 
2.0%
Chantaje (feat. Maluma) 1
 
2.0%
Rockabye (feat. Sean Paul & Anne-Marie) 1
 
2.0%
Work from Home (feat. Ty Dolla $ign) 1
 
2.0%
Other values (34) 34
68.0%

Length

2023-05-17T01:40:07.914307image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
feat 9
 
4.9%
you 8
 
4.4%
8
 
4.4%
taki 6
 
3.3%
gomez 4
 
2.2%
selena 4
 
2.2%
calma 3
 
1.6%
this 3
 
1.6%
b 3
 
1.6%
the 3
 
1.6%
Other values (105) 132
72.1%

Most occurring characters

ValueCountFrequency (%)
133
 
13.7%
a 91
 
9.4%
e 86
 
8.9%
o 48
 
5.0%
i 43
 
4.4%
n 42
 
4.3%
t 38
 
3.9%
r 37
 
3.8%
l 29
 
3.0%
u 29
 
3.0%
Other values (55) 392
40.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 613
63.3%
Uppercase Letter 160
 
16.5%
Space Separator 133
 
13.7%
Other Punctuation 17
 
1.8%
Open Punctuation 15
 
1.5%
Close Punctuation 15
 
1.5%
Dash Punctuation 5
 
0.5%
Other Letter 5
 
0.5%
Decimal Number 4
 
0.4%
Currency Symbol 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 91
14.8%
e 86
14.0%
o 48
 
7.8%
i 43
 
7.0%
n 42
 
6.9%
t 38
 
6.2%
r 37
 
6.0%
l 29
 
4.7%
u 29
 
4.7%
h 26
 
4.2%
Other values (16) 144
23.5%
Uppercase Letter
ValueCountFrequency (%)
T 19
11.9%
S 19
11.9%
C 17
10.6%
B 16
10.0%
W 9
 
5.6%
A 9
 
5.6%
L 9
 
5.6%
Y 8
 
5.0%
G 8
 
5.0%
F 7
 
4.4%
Other values (10) 39
24.4%
Other Letter
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Other Punctuation
ValueCountFrequency (%)
. 9
52.9%
& 4
23.5%
, 3
 
17.6%
' 1
 
5.9%
Decimal Number
ValueCountFrequency (%)
0 2
50.0%
1 1
25.0%
2 1
25.0%
Open Punctuation
ValueCountFrequency (%)
( 14
93.3%
[ 1
 
6.7%
Close Punctuation
ValueCountFrequency (%)
) 14
93.3%
] 1
 
6.7%
Space Separator
ValueCountFrequency (%)
133
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 773
79.9%
Common 190
 
19.6%
Hangul 5
 
0.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 91
 
11.8%
e 86
 
11.1%
o 48
 
6.2%
i 43
 
5.6%
n 42
 
5.4%
t 38
 
4.9%
r 37
 
4.8%
l 29
 
3.8%
u 29
 
3.8%
h 26
 
3.4%
Other values (36) 304
39.3%
Common
ValueCountFrequency (%)
133
70.0%
( 14
 
7.4%
) 14
 
7.4%
. 9
 
4.7%
- 5
 
2.6%
& 4
 
2.1%
, 3
 
1.6%
0 2
 
1.1%
] 1
 
0.5%
1 1
 
0.5%
Other values (4) 4
 
2.1%
Hangul
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 962
99.4%
Hangul 5
 
0.5%
None 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
133
 
13.8%
a 91
 
9.5%
e 86
 
8.9%
o 48
 
5.0%
i 43
 
4.5%
n 42
 
4.4%
t 38
 
4.0%
r 37
 
3.8%
l 29
 
3.0%
u 29
 
3.0%
Other values (49) 386
40.1%
Hangul
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
None
ValueCountFrequency (%)
é 1
100.0%

Danceability
Real number (ℝ)

Distinct41
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.70998
Minimum0.399
Maximum0.941
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-05-17T01:40:07.999751image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.399
5-th percentile0.47295
Q10.65425
median0.7275
Q30.7975
95-th percentile0.8542
Maximum0.941
Range0.542
Interquartile range (IQR)0.14325

Descriptive statistics

Standard deviation0.12158318
Coefficient of variation (CV)0.17124874
Kurtosis0.21751576
Mean0.70998
Median Absolute Deviation (MAD)0.0745
Skewness-0.66982951
Sum35.499
Variance0.014782469
MonotonicityNot monotonic
2023-05-17T01:40:08.093910image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0.723 3
 
6.0%
0.842 3
 
6.0%
0.655 2
 
4.0%
0.631 2
 
4.0%
0.689 2
 
4.0%
0.826 2
 
4.0%
0.728 2
 
4.0%
0.852 1
 
2.0%
0.717 1
 
2.0%
0.803 1
 
2.0%
Other values (31) 31
62.0%
ValueCountFrequency (%)
0.399 1
2.0%
0.444 1
2.0%
0.468 1
2.0%
0.479 1
2.0%
0.489 1
2.0%
0.547 1
2.0%
0.548 1
2.0%
0.589 1
2.0%
0.599 1
2.0%
0.631 2
4.0%
ValueCountFrequency (%)
0.941 1
 
2.0%
0.918 1
 
2.0%
0.856 1
 
2.0%
0.852 1
 
2.0%
0.842 3
6.0%
0.826 2
4.0%
0.825 1
 
2.0%
0.814 1
 
2.0%
0.807 1
 
2.0%
0.803 1
 
2.0%

Energy
Real number (ℝ)

Distinct42
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.702138
Minimum0.0739
Maximum0.937
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-05-17T01:40:08.187032image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.0739
5-th percentile0.41805
Q10.59825
median0.764
Q30.807
95-th percentile0.9261
Maximum0.937
Range0.8631
Interquartile range (IQR)0.20875

Descriptive statistics

Standard deviation0.17138259
Coefficient of variation (CV)0.24408676
Kurtosis2.3125648
Mean0.702138
Median Absolute Deviation (MAD)0.1015
Skewness-1.2365294
Sum35.1069
Variance0.029371993
MonotonicityNot monotonic
2023-05-17T01:40:08.278499image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0.801 3
 
6.0%
0.797 2
 
4.0%
0.927 2
 
4.0%
0.481 2
 
4.0%
0.809 2
 
4.0%
0.764 2
 
4.0%
0.585 2
 
4.0%
0.7 1
 
2.0%
0.773 1
 
2.0%
0.73 1
 
2.0%
Other values (32) 32
64.0%
ValueCountFrequency (%)
0.0739 1
2.0%
0.387 1
2.0%
0.396 1
2.0%
0.445 1
2.0%
0.448 1
2.0%
0.481 2
4.0%
0.545 1
2.0%
0.563 1
2.0%
0.585 2
4.0%
0.587 1
2.0%
ValueCountFrequency (%)
0.937 1
2.0%
0.927 2
4.0%
0.925 1
2.0%
0.887 1
2.0%
0.885 1
2.0%
0.871 1
2.0%
0.86 1
2.0%
0.859 1
2.0%
0.819 1
2.0%
0.813 1
2.0%

Key
Real number (ℝ)

Distinct11
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.08
Minimum0
Maximum11
Zeros3
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-05-17T01:40:08.350970image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.45
Q12.5
median7
Q39
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation3.4983378
Coefficient of variation (CV)0.57538451
Kurtosis-1.197745
Mean6.08
Median Absolute Deviation (MAD)3
Skewness-0.36145005
Sum304
Variance12.238367
MonotonicityNot monotonic
2023-05-17T01:40:08.413355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
8 7
14.0%
9 6
12.0%
7 6
12.0%
2 5
10.0%
1 5
10.0%
10 5
10.0%
11 4
8.0%
4 4
8.0%
0 3
6.0%
5 3
6.0%
ValueCountFrequency (%)
0 3
6.0%
1 5
10.0%
2 5
10.0%
4 4
8.0%
5 3
6.0%
6 2
 
4.0%
7 6
12.0%
8 7
14.0%
9 6
12.0%
10 5
10.0%
ValueCountFrequency (%)
11 4
8.0%
10 5
10.0%
9 6
12.0%
8 7
14.0%
7 6
12.0%
6 2
 
4.0%
5 3
6.0%
4 4
8.0%
2 5
10.0%
1 5
10.0%

Loudness
Real number (ℝ)

Distinct43
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.31906
Minimum-13.961
Maximum-2.652
Zeros0
Zeros (%)0.0%
Negative50
Negative (%)100.0%
Memory size2.8 KiB
2023-05-17T01:40:08.492795image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-13.961
5-th percentile-9.85525
Q1-6.26475
median-4.804
Q3-3.68325
95-th percentile-2.8248
Maximum-2.652
Range11.309
Interquartile range (IQR)2.5815

Descriptive statistics

Standard deviation2.3767322
Coefficient of variation (CV)-0.44683313
Kurtosis3.5479009
Mean-5.31906
Median Absolute Deviation (MAD)1.31
Skewness-1.6794373
Sum-265.953
Variance5.648856
MonotonicityNot monotonic
2023-05-17T01:40:08.580834image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
-4.167 3
 
6.0%
-4.787 2
 
4.0%
-2.787 2
 
4.0%
-7.503 2
 
4.0%
-3.081 2
 
4.0%
-4.341 2
 
4.0%
-6.021 1
 
2.0%
-2.921 1
 
2.0%
-4.729 1
 
2.0%
-5.861 1
 
2.0%
Other values (33) 33
66.0%
ValueCountFrequency (%)
-13.961 1
2.0%
-11.92 1
2.0%
-11.266 1
2.0%
-8.131 1
2.0%
-8.053 1
2.0%
-7.503 2
4.0%
-7.346 1
2.0%
-7.223 1
2.0%
-7.055 1
2.0%
-6.633 1
2.0%
ValueCountFrequency (%)
-2.652 1
2.0%
-2.787 2
4.0%
-2.871 1
2.0%
-2.88 1
2.0%
-2.921 1
2.0%
-3.023 1
2.0%
-3.081 2
4.0%
-3.183 1
2.0%
-3.4 1
2.0%
-3.503 1
2.0%

Speechiness
Real number (ℝ)

Distinct43
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.092564
Minimum0.0232
Maximum0.341
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-05-17T01:40:08.666879image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.0232
5-th percentile0.030445
Q10.04785
median0.06345
Q30.12475
95-th percentile0.228
Maximum0.341
Range0.3178
Interquartile range (IQR)0.0769

Descriptive statistics

Standard deviation0.072001147
Coefficient of variation (CV)0.77785259
Kurtosis2.6190593
Mean0.092564
Median Absolute Deviation (MAD)0.0205
Skewness1.7166665
Sum4.6282
Variance0.0051841652
MonotonicityNot monotonic
2023-05-17T01:40:08.752756image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0.228 3
 
6.0%
0.153 2
 
4.0%
0.0332 2
 
4.0%
0.0815 2
 
4.0%
0.0625 2
 
4.0%
0.0522 2
 
4.0%
0.0694 1
 
2.0%
0.0776 1
 
2.0%
0.0568 1
 
2.0%
0.0432 1
 
2.0%
Other values (33) 33
66.0%
ValueCountFrequency (%)
0.0232 1
2.0%
0.0292 1
2.0%
0.0295 1
2.0%
0.0316 1
2.0%
0.0332 2
4.0%
0.0334 1
2.0%
0.035 1
2.0%
0.0382 1
2.0%
0.0427 1
2.0%
0.0432 1
2.0%
ValueCountFrequency (%)
0.341 1
 
2.0%
0.286 1
 
2.0%
0.228 3
6.0%
0.227 1
 
2.0%
0.153 2
4.0%
0.147 1
 
2.0%
0.141 1
 
2.0%
0.137 1
 
2.0%
0.134 1
 
2.0%
0.128 1
 
2.0%

Acousticness
Real number (ℝ)

Distinct43
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2037234
Minimum0.00261
Maximum0.948
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-05-17T01:40:08.842763image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.00261
5-th percentile0.00346
Q10.040725
median0.157
Q30.33525
95-th percentile0.603
Maximum0.948
Range0.94539
Interquartile range (IQR)0.294525

Descriptive statistics

Standard deviation0.21821352
Coefficient of variation (CV)1.0711265
Kurtosis2.4642749
Mean0.2037234
Median Absolute Deviation (MAD)0.12795
Skewness1.5524364
Sum10.18617
Variance0.047617142
MonotonicityNot monotonic
2023-05-17T01:40:08.930230image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0.157 3
 
6.0%
0.198 2
 
4.0%
0.199 2
 
4.0%
0.369 2
 
4.0%
0.00346 2
 
4.0%
0.337 2
 
4.0%
0.00261 1
 
2.0%
0.187 1
 
2.0%
0.383 1
 
2.0%
0.103 1
 
2.0%
Other values (33) 33
66.0%
ValueCountFrequency (%)
0.00261 1
2.0%
0.00314 1
2.0%
0.00346 2
4.0%
0.00417 1
2.0%
0.00492 1
2.0%
0.0062 1
2.0%
0.00801 1
2.0%
0.0168 1
2.0%
0.0197 1
2.0%
0.0281 1
2.0%
ValueCountFrequency (%)
0.948 1
2.0%
0.829 1
2.0%
0.621 1
2.0%
0.581 1
2.0%
0.545 1
2.0%
0.474 1
2.0%
0.401 1
2.0%
0.383 1
2.0%
0.369 2
4.0%
0.365 1
2.0%

Instrumentalness
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0048180554
Minimum0
Maximum0.119
Zeros23
Zeros (%)46.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-05-17T01:40:09.006614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.335 × 10-6
Q31.36925 × 10-5
95-th percentile0.00123
Maximum0.119
Range0.119
Interquartile range (IQR)1.36925 × 10-5

Descriptive statistics

Standard deviation0.023545166
Coefficient of variation (CV)4.8868607
Kurtosis22.325355
Mean0.0048180554
Median Absolute Deviation (MAD)1.335 × 10-6
Skewness4.8404928
Sum0.24090277
Variance0.00055437482
MonotonicityNot monotonic
2023-05-17T01:40:09.083793image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 23
46.0%
4.82 × 10-63
 
6.0%
7.28 × 10-62
 
4.0%
0.00123 2
 
4.0%
0.119 2
 
4.0%
1.03 × 10-62
 
4.0%
2.88 × 10-51
 
2.0%
3.85 × 10-61
 
2.0%
9.33 × 10-51
 
2.0%
2.87 × 10-61
 
2.0%
Other values (12) 12
24.0%
ValueCountFrequency (%)
0 23
46.0%
1.03 × 10-62
 
4.0%
1.64 × 10-61
 
2.0%
1.94 × 10-61
 
2.0%
2.87 × 10-61
 
2.0%
3.68 × 10-61
 
2.0%
3.85 × 10-61
 
2.0%
3.94 × 10-61
 
2.0%
4.82 × 10-63
 
6.0%
7.28 × 10-62
 
4.0%
ValueCountFrequency (%)
0.119 2
4.0%
0.00123 2
4.0%
9.33 × 10-51
2.0%
8.15 × 10-51
2.0%
6.07 × 10-51
2.0%
3.05 × 10-51
2.0%
3 × 10-51
2.0%
2.88 × 10-51
2.0%
2.29 × 10-51
2.0%
2.25 × 10-51
2.0%

Liveness
Real number (ℝ)

Distinct43
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.148822
Minimum0.0344
Maximum0.565
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-05-17T01:40:09.546968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.0344
5-th percentile0.05866
Q10.067375
median0.109
Q30.1585
95-th percentile0.4651
Maximum0.565
Range0.5306
Interquartile range (IQR)0.091125

Descriptive statistics

Standard deviation0.12577595
Coefficient of variation (CV)0.84514354
Kurtosis4.7597515
Mean0.148822
Median Absolute Deviation (MAD)0.04335
Skewness2.2649892
Sum7.4411
Variance0.01581959
MonotonicityNot monotonic
2023-05-17T01:40:09.636631image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0.0642 3
 
6.0%
0.067 2
 
4.0%
0.148 2
 
4.0%
0.0649 2
 
4.0%
0.565 2
 
4.0%
0.161 2
 
4.0%
0.153 1
 
2.0%
0.159 1
 
2.0%
0.398 1
 
2.0%
0.0644 1
 
2.0%
Other values (33) 33
66.0%
ValueCountFrequency (%)
0.0344 1
 
2.0%
0.0451 1
 
2.0%
0.0574 1
 
2.0%
0.0602 1
 
2.0%
0.0642 3
6.0%
0.0644 1
 
2.0%
0.0649 2
4.0%
0.0663 1
 
2.0%
0.067 2
4.0%
0.0685 1
 
2.0%
ValueCountFrequency (%)
0.565 2
4.0%
0.52 1
2.0%
0.398 1
2.0%
0.354 1
2.0%
0.299 1
2.0%
0.255 1
2.0%
0.184 1
2.0%
0.179 1
2.0%
0.165 1
2.0%
0.161 2
4.0%

Valence
Real number (ℝ)

Distinct41
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.59862
Minimum0.158
Maximum0.969
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-05-17T01:40:09.724733image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.158
5-th percentile0.20175
Q10.36725
median0.617
Q30.82125
95-th percentile0.961
Maximum0.969
Range0.811
Interquartile range (IQR)0.454

Descriptive statistics

Standard deviation0.25013824
Coefficient of variation (CV)0.41785814
Kurtosis-1.1606165
Mean0.59862
Median Absolute Deviation (MAD)0.218
Skewness-0.18539332
Sum29.931
Variance0.062569138
MonotonicityNot monotonic
2023-05-17T01:40:09.813072image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0.839 3
 
6.0%
0.617 3
 
6.0%
0.761 2
 
4.0%
0.283 2
 
4.0%
0.274 2
 
4.0%
0.961 2
 
4.0%
0.465 2
 
4.0%
0.535 1
 
2.0%
0.608 1
 
2.0%
0.907 1
 
2.0%
Other values (31) 31
62.0%
ValueCountFrequency (%)
0.158 1
2.0%
0.159 1
2.0%
0.168 1
2.0%
0.243 1
2.0%
0.25 1
2.0%
0.274 2
4.0%
0.283 2
4.0%
0.288 1
2.0%
0.324 1
2.0%
0.352 1
2.0%
ValueCountFrequency (%)
0.969 1
 
2.0%
0.965 1
 
2.0%
0.961 2
4.0%
0.931 1
 
2.0%
0.928 1
 
2.0%
0.908 1
 
2.0%
0.907 1
 
2.0%
0.884 1
 
2.0%
0.839 3
6.0%
0.831 1
 
2.0%

Tempo
Real number (ℝ)

Distinct43
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.7097
Minimum65.043
Maximum199.956
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-05-17T01:40:09.904514image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum65.043
5-th percentile79.44755
Q195.0215
median102.018
Q3126.43025
95-th percentile178.8707
Maximum199.956
Range134.913
Interquartile range (IQR)31.40875

Descriptive statistics

Standard deviation31.843524
Coefficient of variation (CV)0.28004228
Kurtosis1.1041879
Mean113.7097
Median Absolute Deviation (MAD)18.6905
Skewness1.2163691
Sum5685.485
Variance1014.01
MonotonicityNot monotonic
2023-05-17T01:40:09.995886image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
95.881 3
 
6.0%
177.928 2
 
4.0%
123.962 2
 
4.0%
80.025 2
 
4.0%
98.007 2
 
4.0%
126.9 2
 
4.0%
116.073 1
 
2.0%
102.034 1
 
2.0%
102.002 1
 
2.0%
105.017 1
 
2.0%
Other values (33) 33
66.0%
ValueCountFrequency (%)
65.043 1
2.0%
74.897 1
2.0%
78.998 1
2.0%
79.997 1
2.0%
80.025 2
4.0%
82.695 1
2.0%
86.989 1
2.0%
90.003 1
2.0%
91.01 1
2.0%
91.017 1
2.0%
ValueCountFrequency (%)
199.956 1
2.0%
199.955 1
2.0%
179.642 1
2.0%
177.928 2
4.0%
169.977 1
2.0%
142.526 1
2.0%
134.052 1
2.0%
132.067 1
2.0%
131.934 1
2.0%
126.994 1
2.0%

Duration_ms
Real number (ℝ)

Distinct43
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean228234.58
Minimum127203
Maximum586076
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-05-17T01:40:10.086231image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum127203
5-th percentile176561
Q1205095.25
median220826.5
Q3237627
95-th percentile272981.3
Maximum586076
Range458873
Interquartile range (IQR)32531.75

Descriptive statistics

Standard deviation60068.891
Coefficient of variation (CV)0.26318926
Kurtosis26.208465
Mean228234.58
Median Absolute Deviation (MAD)16800.5
Skewness4.3294428
Sum11411729
Variance3.6082716 × 109
MonotonicityNot monotonic
2023-05-17T01:40:10.172998image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
212500 3
 
6.0%
229360 2
 
4.0%
222160 2
 
4.0%
229526 2
 
4.0%
176561 2
 
4.0%
237627 2
 
4.0%
209320 1
 
2.0%
195840 1
 
2.0%
251173 1
 
2.0%
214480 1
 
2.0%
Other values (33) 33
66.0%
ValueCountFrequency (%)
127203 1
2.0%
147781 1
2.0%
176561 2
4.0%
185040 1
2.0%
187920 1
2.0%
187973 1
2.0%
193227 1
2.0%
195840 1
2.0%
200787 1
2.0%
202333 1
2.0%
ValueCountFrequency (%)
586076 1
2.0%
281560 1
2.0%
275693 1
2.0%
269667 1
2.0%
266627 1
2.0%
263400 1
2.0%
263373 1
2.0%
257840 1
2.0%
253520 1
2.0%
252733 1
2.0%

Title
Categorical

HIGH CORRELATION  UNIFORM 

Distinct42
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
DJ Snake - Taki Taki ft. Selena Gomez, Ozuna, Cardi B (Official Music Video)
 
3
Luis Fonsi - Despacito ft. Daddy Yankee
 
2
Calvin Harris, Rihanna - This Is What You Came For (Official Video) ft. Rihanna
 
2
Wiz Khalifa - See You Again ft. Charlie Puth [Official Video] Furious 7 Soundtrack
 
2
Major Lazer & DJ Snake - Lean On (feat. MØ) (Official Music Video)
 
2
Other values (37)
39 

Length

Max length100
Median length65
Mean length54.44
Min length19

Characters and Unicode

Total characters2722
Distinct characters92
Distinct categories14 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)70.0%

Sample

1st rowLuis Fonsi - Despacito ft. Daddy Yankee
2nd rowLuis Fonsi - Despacito ft. Daddy Yankee
3rd rowEd Sheeran - Shape of You (Official Music Video)
4th rowWiz Khalifa - See You Again ft. Charlie Puth [Official Video] Furious 7 Soundtrack
5th rowWiz Khalifa - See You Again ft. Charlie Puth [Official Video] Furious 7 Soundtrack

Common Values

ValueCountFrequency (%)
DJ Snake - Taki Taki ft. Selena Gomez, Ozuna, Cardi B (Official Music Video) 3
 
6.0%
Luis Fonsi - Despacito ft. Daddy Yankee 2
 
4.0%
Calvin Harris, Rihanna - This Is What You Came For (Official Video) ft. Rihanna 2
 
4.0%
Wiz Khalifa - See You Again ft. Charlie Puth [Official Video] Furious 7 Soundtrack 2
 
4.0%
Major Lazer & DJ Snake - Lean On (feat. MØ) (Official Music Video) 2
 
4.0%
Pedro Capó, Farruko - Calma (Remix - Official Video) 2
 
4.0%
Passenger | Let Her Go (Official Video) 2
 
4.0%
Shakira - Chantaje (Official Video) ft. Maluma 1
 
2.0%
Clean Bandit - Rockabye (feat. Sean Paul & Anne-Marie) [Official Video] 1
 
2.0%
Fifth Harmony - Work from Home (Official Video) ft. Ty Dolla $ign 1
 
2.0%
Other values (32) 32
64.0%

Length

2023-05-17T01:40:10.267552image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
58
 
12.1%
video 34
 
7.1%
official 34
 
7.1%
ft 16
 
3.3%
music 12
 
2.5%
you 8
 
1.7%
taki 6
 
1.3%
dj 5
 
1.0%
ozuna 5
 
1.0%
rihanna 5
 
1.0%
Other values (201) 295
61.7%

Most occurring characters

ValueCountFrequency (%)
428
 
15.7%
a 206
 
7.6%
i 205
 
7.5%
e 180
 
6.6%
o 118
 
4.3%
n 105
 
3.9%
f 97
 
3.6%
l 90
 
3.3%
r 86
 
3.2%
s 68
 
2.5%
Other values (82) 1139
41.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1627
59.8%
Uppercase Letter 447
 
16.4%
Space Separator 428
 
15.7%
Other Punctuation 50
 
1.8%
Dash Punctuation 46
 
1.7%
Close Punctuation 44
 
1.6%
Open Punctuation 44
 
1.6%
Other Letter 14
 
0.5%
Decimal Number 7
 
0.3%
Math Symbol 5
 
0.2%
Other values (4) 10
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 206
12.7%
i 205
12.6%
e 180
11.1%
o 118
 
7.3%
n 105
 
6.5%
f 97
 
6.0%
l 90
 
5.5%
r 86
 
5.3%
s 68
 
4.2%
u 67
 
4.1%
Other values (19) 405
24.9%
Uppercase Letter
ValueCountFrequency (%)
O 48
 
10.7%
S 40
 
8.9%
V 35
 
7.8%
C 32
 
7.2%
M 29
 
6.5%
B 23
 
5.1%
T 22
 
4.9%
D 21
 
4.7%
R 20
 
4.5%
L 18
 
4.0%
Other values (15) 159
35.6%
Other Letter
ValueCountFrequency (%)
2
14.3%
1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
Other values (3) 3
21.4%
Other Punctuation
ValueCountFrequency (%)
. 20
40.0%
, 19
38.0%
& 7
 
14.0%
: 2
 
4.0%
/ 1
 
2.0%
' 1
 
2.0%
Decimal Number
ValueCountFrequency (%)
7 2
28.6%
0 2
28.6%
5 1
14.3%
2 1
14.3%
1 1
14.3%
Close Punctuation
ValueCountFrequency (%)
) 38
86.4%
] 6
 
13.6%
Open Punctuation
ValueCountFrequency (%)
( 38
86.4%
[ 6
 
13.6%
Spacing Mark
ValueCountFrequency (%)
3
60.0%
2
40.0%
Other Symbol
ValueCountFrequency (%)
1
50.0%
1
50.0%
Nonspacing Mark
ValueCountFrequency (%)
1
50.0%
1
50.0%
Space Separator
ValueCountFrequency (%)
428
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 46
100.0%
Math Symbol
ValueCountFrequency (%)
| 5
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2074
76.2%
Common 627
 
23.0%
Devanagari 16
 
0.6%
Hangul 5
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 206
 
9.9%
i 205
 
9.9%
e 180
 
8.7%
o 118
 
5.7%
n 105
 
5.1%
f 97
 
4.7%
l 90
 
4.3%
r 86
 
4.1%
s 68
 
3.3%
u 67
 
3.2%
Other values (44) 852
41.1%
Common
ValueCountFrequency (%)
428
68.3%
- 46
 
7.3%
) 38
 
6.1%
( 38
 
6.1%
. 20
 
3.2%
, 19
 
3.0%
& 7
 
1.1%
] 6
 
1.0%
[ 6
 
1.0%
| 5
 
0.8%
Other values (11) 14
 
2.2%
Devanagari
ValueCountFrequency (%)
3
18.8%
2
12.5%
2
12.5%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
Other values (2) 2
12.5%
Hangul
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2693
98.9%
Devanagari 16
 
0.6%
None 6
 
0.2%
Hangul 5
 
0.2%
Letterlike Symbols 1
 
< 0.1%
Dingbats 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
428
 
15.9%
a 206
 
7.6%
i 205
 
7.6%
e 180
 
6.7%
o 118
 
4.4%
n 105
 
3.9%
f 97
 
3.6%
l 90
 
3.3%
r 86
 
3.2%
s 68
 
2.5%
Other values (59) 1110
41.2%
Devanagari
ValueCountFrequency (%)
3
18.8%
2
12.5%
2
12.5%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
Other values (2) 2
12.5%
None
ValueCountFrequency (%)
ó 2
33.3%
Ø 2
33.3%
í 1
16.7%
ñ 1
16.7%
Hangul
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Letterlike Symbols
ValueCountFrequency (%)
1
100.0%
Dingbats
ValueCountFrequency (%)
1
100.0%

Channel
Categorical

HIGH CORRELATION  UNIFORM 

Distinct35
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
Cocomelon - Nursery Rhymes
 
3
Ed Sheeran
 
3
DJSnakeVEVO
 
3
LuisFonsiVEVO
 
2
JustinBieberVEVO
 
2
Other values (30)
37 

Length

Max length26
Median length18
Mean length14.02
Min length7

Characters and Unicode

Total characters701
Distinct characters50
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)46.0%

Sample

1st rowLuisFonsiVEVO
2nd rowLuisFonsiVEVO
3rd rowEd Sheeran
4th rowWiz Khalifa Music
5th rowWiz Khalifa Music

Common Values

ValueCountFrequency (%)
Cocomelon - Nursery Rhymes 3
 
6.0%
Ed Sheeran 3
 
6.0%
DJSnakeVEVO 3
 
6.0%
LuisFonsiVEVO 2
 
4.0%
JustinBieberVEVO 2
 
4.0%
CalvinHarrisVEVO 2
 
4.0%
Major Lazer Official 2
 
4.0%
Passenger 2
 
4.0%
capoVEVO 2
 
4.0%
shakiraVEVO 2
 
4.0%
Other values (25) 27
54.0%

Length

2023-05-17T01:40:10.375524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cocomelon 3
 
3.7%
nursery 3
 
3.7%
rhymes 3
 
3.7%
ed 3
 
3.7%
sheeran 3
 
3.7%
djsnakevevo 3
 
3.7%
3
 
3.7%
passenger 2
 
2.4%
music 2
 
2.4%
wiz 2
 
2.4%
Other values (45) 55
67.1%

Most occurring characters

ValueCountFrequency (%)
a 57
 
8.1%
V 56
 
8.0%
e 51
 
7.3%
i 44
 
6.3%
r 39
 
5.6%
n 37
 
5.3%
E 34
 
4.9%
33
 
4.7%
O 31
 
4.4%
s 29
 
4.1%
Other values (40) 290
41.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 444
63.3%
Uppercase Letter 219
31.2%
Space Separator 33
 
4.7%
Dash Punctuation 4
 
0.6%
Decimal Number 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 57
12.8%
e 51
11.5%
i 44
9.9%
r 39
 
8.8%
n 37
 
8.3%
s 29
 
6.5%
o 27
 
6.1%
l 23
 
5.2%
h 16
 
3.6%
y 15
 
3.4%
Other values (15) 106
23.9%
Uppercase Letter
ValueCountFrequency (%)
V 56
25.6%
E 34
15.5%
O 31
14.2%
S 12
 
5.5%
M 9
 
4.1%
C 7
 
3.2%
T 6
 
2.7%
L 6
 
2.7%
R 6
 
2.7%
D 6
 
2.7%
Other values (12) 46
21.0%
Space Separator
ValueCountFrequency (%)
33
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Decimal Number
ValueCountFrequency (%)
5 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 663
94.6%
Common 38
 
5.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 57
 
8.6%
V 56
 
8.4%
e 51
 
7.7%
i 44
 
6.6%
r 39
 
5.9%
n 37
 
5.6%
E 34
 
5.1%
O 31
 
4.7%
s 29
 
4.4%
o 27
 
4.1%
Other values (37) 258
38.9%
Common
ValueCountFrequency (%)
33
86.8%
- 4
 
10.5%
5 1
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 701
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 57
 
8.1%
V 56
 
8.0%
e 51
 
7.3%
i 44
 
6.3%
r 39
 
5.6%
n 37
 
5.3%
E 34
 
4.9%
33
 
4.7%
O 31
 
4.4%
s 29
 
4.1%
Other values (40) 290
41.4%

Views
Real number (ℝ)

Distinct48
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4317244 × 109
Minimum2.2837481 × 109
Maximum8.0796494 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-05-17T01:40:10.464853image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2.2837481 × 109
5-th percentile2.3613016 × 109
Q12.6260299 × 109
median3.0124999 × 109
Q33.531993 × 109
95-th percentile5.8478284 × 109
Maximum8.0796494 × 109
Range5.7959013 × 109
Interquartile range (IQR)9.0596308 × 108

Descriptive statistics

Standard deviation1.3151666 × 109
Coefficient of variation (CV)0.38323783
Kurtosis4.9622199
Mean3.4317244 × 109
Median Absolute Deviation (MAD)4.3975906 × 108
Skewness2.1771368
Sum1.7158622 × 1011
Variance1.7296633 × 1018
MonotonicityDecreasing
2023-05-17T01:40:10.554378image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
3376086171 2
 
4.0%
2626029906 2
 
4.0%
8079649362 1
 
2.0%
2925709616 1
 
2.0%
2809699009 1
 
2.0%
2805793003 1
 
2.0%
2761229559 1
 
2.0%
2666673882 1
 
2.0%
2661008889 1
 
2.0%
2661002503 1
 
2.0%
Other values (38) 38
76.0%
ValueCountFrequency (%)
2283748076 1
2.0%
2315953928 1
2.0%
2354418112 1
2.0%
2369714846 1
2.0%
2414201310 1
2.0%
2414201716 1
2.0%
2414202125 1
2.0%
2447960265 1
2.0%
2539156924 1
2.0%
2563945395 1
2.0%
ValueCountFrequency (%)
8079649362 1
2.0%
8079646911 1
2.0%
5908398479 1
2.0%
5773798407 1
2.0%
5773797147 1
2.0%
4898831101 1
2.0%
4821016218 1
2.0%
4679767471 1
2.0%
3817733132 1
2.0%
3725748519 1
2.0%

Likes
Real number (ℝ)

Distinct48
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17734660
Minimum6604353
Maximum50788652
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-05-17T01:40:10.650274image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum6604353
5-th percentile9197448.2
Q112432697
median15361684
Q318776038
95-th percentile40147649
Maximum50788652
Range44184299
Interquartile range (IQR)6343341.8

Descriptive statistics

Standard deviation9658910.6
Coefficient of variation (CV)0.54463466
Kurtosis4.7424395
Mean17734660
Median Absolute Deviation (MAD)3414333.5
Skewness2.1554919
Sum8.8673301 × 108
Variance9.3294554 × 1013
MonotonicityNot monotonic
2023-05-17T01:40:10.741870image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
15361684 2
 
4.0%
10603656 2
 
4.0%
50788652 1
 
2.0%
13643149 1
 
2.0%
16730579 1
 
2.0%
10370533 1
 
2.0%
13333328 1
 
2.0%
11869546 1
 
2.0%
12824789 1
 
2.0%
12824730 1
 
2.0%
Other values (38) 38
76.0%
ValueCountFrequency (%)
6604353 1
2.0%
8195497 1
2.0%
8943443 1
2.0%
9507899 1
2.0%
10159799 1
2.0%
10169573 1
2.0%
10370533 1
2.0%
10603656 2
4.0%
11040774 1
2.0%
11869546 1
2.0%
ValueCountFrequency (%)
50788652 1
2.0%
50788626 1
2.0%
40147674 1
2.0%
40147618 1
2.0%
31047780 1
2.0%
26446178 1
2.0%
26399133 1
2.0%
23212268 1
2.0%
20483444 1
2.0%
20327883 1
2.0%

Licensed
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
True
46 
False
 
4
ValueCountFrequency (%)
True 46
92.0%
False 4
 
8.0%
2023-05-17T01:40:10.822467image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Stream
Real number (ℝ)

Distinct43
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2324983 × 109
Minimum22632716
Maximum3.3620052 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-05-17T01:40:10.892396image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum22632716
5-th percentile69704253
Q18.0295127 × 108
median1.3797651 × 109
Q31.6758385 × 109
95-th percentile2.2414298 × 109
Maximum3.3620052 × 109
Range3.3393725 × 109
Interquartile range (IQR)8.7288722 × 108

Descriptive statistics

Standard deviation6.8110617 × 108
Coefficient of variation (CV)0.55262238
Kurtosis0.86929116
Mean1.2324983 × 109
Median Absolute Deviation (MAD)3.3330558 × 108
Skewness0.19111968
Sum6.1624917 × 1010
Variance4.6390562 × 1017
MonotonicityNot monotonic
2023-05-17T01:40:10.981582image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
1379765140 3
 
6.0%
1506598267 2
 
4.0%
1389243583 2
 
4.0%
1521254554 2
 
4.0%
1713070715 2
 
4.0%
1048932845 2
 
4.0%
1807370674 1
 
2.0%
859799057 1
 
2.0%
1312649614 1
 
2.0%
1270912565 1
 
2.0%
Other values (33) 33
66.0%
ValueCountFrequency (%)
22632716 1
2.0%
46554351 1
2.0%
58470533 1
2.0%
83434355 1
2.0%
96075345 1
2.0%
135121858 1
2.0%
268969064 1
2.0%
370991124 1
2.0%
625188054 1
2.0%
629918488 1
2.0%
ValueCountFrequency (%)
3362005201 1
2.0%
2369272335 1
2.0%
2312689776 1
2.0%
2154334378 1
2.0%
1833638048 1
2.0%
1807370674 1
2.0%
1805319715 1
2.0%
1785368454 1
2.0%
1740759086 1
2.0%
1713070715 2
4.0%

Interactions

2023-05-17T01:40:05.949557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:49.096532image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:50.268048image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:51.422784image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:52.458405image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:53.479687image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:54.527264image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:56.585721image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:57.676888image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:58.973099image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:00.295067image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:01.476122image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:02.562529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:03.596860image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:04.940123image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:06.027305image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:49.191715image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:50.342953image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:51.496800image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:52.532286image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:53.554618image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:54.604424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:56.659184image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:57.755722image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:59.054005image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:00.374712image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:01.554234image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:02.636608image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:03.672243image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:05.012177image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:06.101270image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:49.273126image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:50.425669image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:51.560482image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:52.598458image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:53.622815image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:54.674361image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:56.726519image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:57.827039image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:59.126427image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:00.448639image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:01.626064image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:02.705824image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:03.742727image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:05.091168image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:06.179803image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:49.348577image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:50.498492image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:51.620055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:52.659534image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:53.685545image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:54.739056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:56.788026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:57.891827image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:59.188389image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:00.528315image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:01.691302image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:02.768064image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:03.806011image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:05.151585image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:06.249073image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:49.422348image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:50.574683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:51.682716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:52.725527image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:53.750896image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:54.806828image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:56.863550image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:57.960545image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:59.271167image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:00.609310image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:01.758742image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:02.832527image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:03.871678image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:05.215507image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:06.319971image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:49.494125image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:50.651086image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:51.747039image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:52.790315image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:53.817940image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:54.875295image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:56.949233image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:58.030202image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:59.337271image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:00.699092image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:01.828329image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:02.901462image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:03.939689image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:05.280966image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:06.395460image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:49.575429image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:50.734989image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:51.820014image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:52.859081image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:53.888885image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:55.912561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:57.024687image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:58.105596image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:59.406687image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:00.775764image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:01.914198image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:02.977582image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:04.309866image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:05.349825image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:06.463959image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:49.647527image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:50.813218image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:51.889126image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:52.923568image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:53.953345image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:55.985093image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:57.091809image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:58.174018image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:59.471966image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:00.867762image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:01.982517image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:03.042754image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:04.375721image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:05.411959image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:06.538733image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:49.726147image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:50.894306image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:51.965766image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:52.992040image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:54.023854image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:56.058870image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:57.173694image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:58.256950image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:59.545905image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:00.944548image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:02.058318image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:03.114188image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:04.447798image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:05.481337image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:06.615826image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:49.799111image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:50.964346image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:52.039356image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:53.056752image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:54.089776image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:56.128504image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:57.249688image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:58.362967image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:59.878215image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:01.015938image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:02.126037image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:03.178985image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:04.515323image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:05.544134image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:06.724984image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:49.878058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:51.043718image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:52.115955image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:53.129543image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:54.164447image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:56.205351image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:57.323981image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:58.475595image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:59.951488image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:01.098120image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:02.203515image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:03.253698image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:04.590898image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:05.617220image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:06.806259image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:49.957290image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:51.123983image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:52.184491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:53.199167image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:54.236132image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:56.281673image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:57.394097image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:58.609112image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:00.025845image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:01.178851image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:02.278041image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:03.324706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:04.662763image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:05.685716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:06.876580image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:50.029732image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:51.199904image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:52.248144image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:53.263157image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:54.300947image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:56.350470image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:57.459861image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:58.704852image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:00.091297image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:01.251687image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:02.346529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:03.389442image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:04.730170image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:05.749698image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:06.952038image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:50.106682image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:51.280788image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:52.323748image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:53.333404image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:54.369886image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:56.421951image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:57.529013image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:58.802903image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:00.158521image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:01.326071image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:02.418890image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:03.458701image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:04.799948image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:05.816720image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:07.031579image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:50.186548image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:51.347877image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:52.385959image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:53.407365image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:54.453720image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:56.489877image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:57.596086image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:39:58.876850image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:00.223621image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:01.396650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:02.486440image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:03.522942image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:04.865444image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-17T01:40:05.879749image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2023-05-17T01:40:11.080526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Unnamed: 0DanceabilityEnergyKeyLoudnessSpeechinessAcousticnessInstrumentalnessLivenessValenceTempoDuration_msViewsLikesStreamArtistTrackTitleChannelLicensed
Unnamed: 01.0000.142-0.3000.061-0.233-0.050-0.0100.2760.143-0.1260.038-0.377-0.250-0.150-0.0150.5120.2100.2570.4830.126
Danceability0.1421.0000.0250.0890.0360.2920.0170.176-0.1130.4730.006-0.062-0.175-0.074-0.1730.0000.4570.3970.3740.000
Energy-0.3000.0251.0000.2090.7450.266-0.3610.0940.0510.240-0.056-0.210-0.340-0.079-0.1040.0000.4030.3010.2780.000
Key0.0610.0890.2091.0000.1770.161-0.0200.0520.093-0.066-0.084-0.170-0.317-0.025-0.0740.0000.4310.4050.3640.000
Loudness-0.2330.0360.7450.1771.0000.075-0.2310.2710.1150.106-0.117-0.150-0.358-0.2090.0380.0000.4030.3050.0000.287
Speechiness-0.0500.2920.2660.1610.0751.000-0.086-0.097-0.4210.177-0.056-0.218-0.1260.3760.0460.2960.4030.4250.5280.457
Acousticness-0.0100.017-0.361-0.020-0.231-0.0861.000-0.321-0.0050.048-0.1050.419-0.013-0.231-0.1380.0000.4310.3290.2130.000
Instrumentalness0.2760.1760.0940.0520.271-0.097-0.3211.0000.006-0.0250.089-0.414-0.209-0.256-0.0550.4790.3540.4080.5590.000
Liveness0.143-0.1130.0510.0930.115-0.421-0.0050.0061.000-0.165-0.054-0.0100.011-0.303-0.0400.0000.3690.4260.3720.461
Valence-0.1260.4730.240-0.0660.1060.1770.048-0.025-0.1651.0000.279-0.1120.051-0.227-0.2950.0000.4570.4690.4310.145
Tempo0.0380.006-0.056-0.084-0.117-0.056-0.1050.089-0.0540.2791.000-0.3140.1500.031-0.1390.0880.4310.3470.3840.000
Duration_ms-0.377-0.062-0.210-0.170-0.150-0.2180.419-0.414-0.010-0.112-0.3141.0000.1620.0050.0060.1140.3650.3700.3570.000
Views-0.250-0.175-0.340-0.317-0.358-0.126-0.013-0.2090.0110.0510.1500.1621.0000.5160.1850.0000.3690.4260.3340.000
Likes-0.150-0.074-0.079-0.025-0.2090.376-0.231-0.256-0.303-0.2270.0310.0050.5161.0000.4980.0000.4030.4360.3930.076
Stream-0.015-0.173-0.104-0.0740.0380.046-0.138-0.055-0.040-0.295-0.1390.0060.1850.4981.0000.0000.4310.4180.3230.000
Artist0.5120.0000.0000.0000.0000.2960.0000.4790.0000.0000.0880.1140.0000.0000.0001.0000.0000.2070.8190.000
Track0.2100.4570.4030.4310.4030.4030.4310.3540.3690.4570.4310.3650.3690.4030.4310.0001.0000.9760.8890.354
Title0.2570.3970.3010.4050.3050.4250.3290.4080.4260.4690.3470.3700.4260.4360.4180.2070.9761.0000.9110.408
Channel0.4830.3740.2780.3640.0000.5280.2130.5590.3720.4310.3840.3570.3340.3930.3230.8190.8890.9111.0000.559
Licensed0.1260.0000.0000.0000.2870.4570.0000.0000.4610.1450.0000.0000.0000.0760.0000.0000.3540.4080.5591.000

Missing values

2023-05-17T01:40:07.185066image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-17T01:40:07.385315image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0ArtistTrackDanceabilityEnergyKeyLoudnessSpeechinessAcousticnessInstrumentalnessLivenessValenceTempoDuration_msTitleChannelViewsLikesLicensedStream
10631147Luis FonsiDespacito0.6550.7972.0-4.7870.15300.198000.0000000.06700.839177.928229360.0Luis Fonsi - Despacito ft. Daddy YankeeLuisFonsiVEVO8.079649e+0950788652.0True1.506598e+09
356365Daddy YankeeDespacito0.6550.7972.0-4.7870.15300.198000.0000000.06700.839177.928229360.0Luis Fonsi - Despacito ft. Daddy YankeeLuisFonsiVEVO8.079647e+0950788626.0True1.506598e+09
1170812452Ed SheeranShape of You0.8250.6521.0-3.1830.08020.581000.0000000.09310.93195.977233713.0Ed Sheeran - Shape of You (Official Music Video)Ed Sheeran5.908398e+0931047780.0True3.362005e+09
1370914580Charlie PuthSee You Again (feat. Charlie Puth)0.6890.48110.0-7.5030.08150.369000.0000010.06490.28380.025229526.0Wiz Khalifa - See You Again ft. Charlie Puth [Official Video] Furious 7 SoundtrackWiz Khalifa Music5.773798e+0940147674.0True1.521255e+09
1172512469Wiz KhalifaSee You Again (feat. Charlie Puth)0.6890.48110.0-7.5030.08150.369000.0000010.06490.28380.025229526.0Wiz Khalifa - See You Again ft. Charlie Puth [Official Video] Furious 7 SoundtrackWiz Khalifa Music5.773797e+0940147618.0True1.521255e+09
1925420303CoComelonWheels on the Bus0.9410.3879.0-11.9200.04270.184000.0000290.15700.965125.021207340.0Wheels on the Bus | CoComelon Nursery Rhymes & Kids SongsCocomelon - Nursery Rhymes4.898831e+0914396841.0True8.343436e+07
1004710686Mark RonsonUptown Funk (feat. Bruno Mars)0.8560.6090.0-7.2230.08240.008010.0000820.03440.928114.988269667.0Mark Ronson - Uptown Funk (Official Video) ft. Bruno MarsMarkRonsonVEVO4.821016e+0920067879.0True1.653820e+09
83978937PSYGangnam Style (강남스타일)0.7270.93711.0-2.8710.28600.004170.0000000.09100.749132.067219493.0PSY - GANGNAM STYLE(강남스타일) M/Vofficialpsy4.679767e+0926399133.0False3.709911e+08
89899569Maroon 5Sugar0.7480.7881.0-7.0550.03340.059100.0000000.08630.884120.076235493.0Maroon 5 - Sugar (Official Music Video)Maroon5VEVO3.817733e+0915340646.0True1.502781e+09
1223913032Katy PerryRoar0.6710.7717.0-4.8210.03160.004920.0000070.35400.43690.003223546.0Katy Perry - Roar (Official)KatyPerryVEVO3.725749e+0915864499.0True8.847210e+08
Unnamed: 0ArtistTrackDanceabilityEnergyKeyLoudnessSpeechinessAcousticnessInstrumentalnessLivenessValenceTempoDuration_msTitleChannelViewsLikesLicensedStream
1441915304Meghan TrainorAll About That Bass0.8070.8879.0-3.7260.05030.05730.0000030.12400.961134.052187920.0Meghan Trainor - All About That Bass (Official Video)MeghanTrainorVEVO2.563945e+099507899.0True7.029070e+08
119126EminemLove The Way You Lie0.7490.92510.0-5.0340.22700.24100.0000000.52000.64186.989263373.0Eminem - Love The Way You Lie ft. RihannaEminemVEVO2.539157e+0912696568.0True1.115479e+09
1466015559Natti NatashaCriminal0.8140.8132.0-3.0230.05610.03000.0000930.25500.83979.997232550.0Natti Natasha ❌ Ozuna - Criminal [Official Video]NATTI NATASHA2.447960e+098195497.0True7.840020e+08
1569516637OzunaTaki Taki (with Selena Gomez, Ozuna & Cardi B)0.8420.8018.0-4.1670.22800.15700.0000050.06420.61795.881212500.0DJ Snake - Taki Taki ft. Selena Gomez, Ozuna, Cardi B (Official Music Video)DJSnakeVEVO2.414202e+0918776043.0True1.379765e+09
1450415389DJ SnakeTaki Taki (feat. Selena Gomez, Ozuna & Cardi B)0.8420.8018.0-4.1670.22800.15700.0000050.06420.61795.881212500.0DJ Snake - Taki Taki ft. Selena Gomez, Ozuna, Cardi B (Official Music Video)DJSnakeVEVO2.414202e+0918776025.0True1.379765e+09
1270613526Selena GomezTaki Taki (with Selena Gomez, Ozuna & Cardi B)0.8420.8018.0-4.1670.22800.15700.0000050.06420.61795.881212500.0DJ Snake - Taki Taki ft. Selena Gomez, Ozuna, Cardi B (Official Music Video)DJSnakeVEVO2.414201e+0918776010.0True1.379765e+09
1319514030Imagine DragonsBeliever0.7760.78010.0-4.3740.12800.06220.0000000.08100.666124.949204347.0Imagine Dragons - Believer (Official Music Video)ImagineDragonsVEVO2.369715e+0920483444.0True2.369272e+09
1456515463DarellTe Boté0.9180.6024.0-3.9860.11500.40100.0000070.08050.15896.518275693.0Casper, Nio García, Darell, Nicky Jam, Bad Bunny, Ozuna - Te Bote Remix (Video Oficial)Flow La Movie2.354418e+0910169573.0True9.607534e+07
1633217286Shawn MendesTreat You Better0.4440.81910.0-4.0780.34100.10600.0000000.10700.74782.695187973.0Shawn Mendes - Treat You BetterShawnMendesVEVO2.315954e+0911040774.0True1.697308e+09
1331714152Ellie GouldingLove Me Like You Do0.4890.5975.0-6.6330.02920.27000.0000000.10500.32495.012253520.0Ellie Goulding - Love Me Like You Do (Official Video)EllieGouldingVEVO2.283748e+0912418131.0True2.689691e+08